Fully Printed and Encapsulated SWCNT-Based Thin Film Transistors via a Combination of R2R Gravure and Inkjet Printing
Bibliographic record
Abstract
Fully printed thin film transistors (TFT) based on poly(9,9-di- n -dodecylfluorene) (PFDD) wrapped semiconducting single walled carbon nanotube (SWCNT) channels are fabricated by a practical route that combines roll-to-roll (R2R) gravure and ink jet printing. SWCNT network density is easily controlled via ink formulation (concentration and polymer:CNT ratio) and jetting conditions (droplet size, drop spacing, and number of printed layers). Optimum inkjet printing conditions are established on Si/SiO 2 in which an ink consisting of 6:1 PFDD:SWCNT ratio with 50 mg L –1 SWCNT concentration printed at a drop spacing of 20 μm results in TFTs with mobilities of ∼25 cm 2 V –1 s –1 and on-/off-current ratios > 10 5 . These conditions yield excellent network uniformity and are used in a fully additive process to fabricate fully printed TFTs on PET substrates with mobility values > 5 cm 2 V –1 s –1 (R2R printed gate electrode and dielectric; inkjet printed channel and source/drain electrodes). An inkjet printed encapsulation layer completes the TFT process (fabricated in bottom gate, top contact TFT configuration) and provides mobilities > 1 cm 2 V –1 s –1 with good operational stability, based on the performance of an inverter circuit. An array of 20 TFTs shows that most have less than 10% variability in terms of threshold voltage, transconductance, on-current, and subthreshold swing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".